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        <h1 class="mt-4 article-title page-title">人工智能复习</h1>
        
        <p class="lead text-gray mt-3">By Anonymous; Published on 2020-03-12</p>
        
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                            <article class="article-text page-content"><h4 id="线性回归"><a href="#线性回归" class="headerlink" title="线性回归"></a>线性回归</h4><p>•单变量线性回归的假设函数、代价函数、优化目标、梯度下降对参数theta的更新（对参数进行同步更新）</p>
<p>•多变量线性回归的假设函数、代价函数、优化目标、梯度下降对参数theta的更新</p>
<p>•学习率的选择（过大或过小）对代价函数变化的影响</p>
<p>•当使用多个特征建模时，为什么要进行特征缩放？</p>
<h4 id="逻辑回归"><a href="#逻辑回归" class="headerlink" title="逻辑回归"></a>逻辑回归</h4><p>•逻辑回归的假设函数、代价函数、优化目标、梯度下降对参数theta的更新</p>
<p>•逻辑回归的预测输出的取值范围及其意义：$0 &lt; h_{\theta}{(x)}&lt;1$</p>
<p>$h_{\theta}{(x)}=p(y=1|x;\theta)$</p>
<p>​                $y=0 \ or \ 1$</p>
<p>•Sigmoid 函数公式及图形</p>
<p>•在什么情况下预测y=1?什么情况下预测y=0? 决策边界是什么？</p>
<p>​        $h_{\theta}{(x)}=g(\theta^Tx)$</p>
<p>​                         $g(z)=\frac{1}{1+e^{-z}}$</p>
<p>​        $y = 1, if \ h_{\theta}{(x)} \geq 0.5$</p>
<p>​                        $\theta^Tx \geq 0$</p>
<p>​        $y = 1, if  \ h_{\theta}{(x)} &lt; 0.5$</p>
<p>​                       $\theta^Tx&lt;0$</p>
<p>•对于K（K&gt;2）分类问题，需要设计（ K ）个二元分类器，给定一个新的样本x，它属于第i类的判断依据： $\mathop {\max }\limits_x h_{\theta}^{(i)}{(x)}$</p>
<h4 id="正则化"><a href="#正则化" class="headerlink" title="正则化"></a>正则化</h4><p>•什么是欠拟合?什么是过拟合？如何改善过拟合？</p>
<p>Options:</p>
<p>​    1. Reduce number of features.</p>
<p>​        ―Manually select which features to keep.</p>
<p>​        ―Model selection algorithm (later in course).</p>
<p>​    2. Regularization.</p>
<p>​        ―Keep all the features, but reduce magnitude/values of parameters  .</p>
<p>​        ―Works well when we have a lot of features, each of which contributes a bit to predicting  </p>
<p>•带有正则化的线性回归的假设函数、代价函数、优化目标、梯度下降</p>
<p>•带有正则化的逻辑回归的假设函数、代价函数、优化目标、梯度下降</p>
<p>•当正则化参数  取值过大或过小时会发生什么情况？</p>
<p>​        $\lambda$ 过大，那么惩罚效果就过大，所以$\theta_i$就会很小，这样就会发生欠拟合。</p>
<p>​        $\lambda$过下，惩罚效果太轻，那么就会过拟合</p>
<h4 id="神经网络"><a href="#神经网络" class="headerlink" title="神经网络"></a>神经网络</h4><p>•神经网络的模型表示，包括每个参数的意义</p>
<p><img src="C:%5CUsers%5Chwy%5CAppData%5CRoaming%5CTypora%5Ctypora-user-images%5Cimage-20191216133246183.png" alt="image-20191216133246183"></p>
<p>•神经网络处理分类问题，样本标签y的维度及取值</p>
<p>​        对于n分类问题，输出为n个单元，即 $y\in R^{(n)}$, 取值按编码的形式展开</p>
<p>•对神经网络计算偏导时，为什么要提出反向传播算法，直接应用梯度下降不行吗？</p>
<p>​        答案是不行的。</p>
<p>​        纵然梯度下降神通广大，但却不是万能的。<strong>梯度下降</strong>可以应对带有明确求导函数的情况，或者说<strong>可以应对那些可以求出误差的情况</strong>，比如逻辑回归，我们可以把它看作是没有隐含层的网络。</p>
<p>​        但对于含有多个隐含层的神经网络，在输出层可以直接求出误差来更新参数，但其中<strong>隐含层的误差是不存在的，因此不能对它直接应用梯度下降</strong>，而是先将误差反向传播至隐含层，然后再应用梯度下降 。</p>
<p>•神经网络前向传播的过程、反向传播、偏导的计算、$\delta_{j}^{(l)}$的含义等</p>
<p>​        第L层第j个神经元上的“误差”</p>
<p>•神经网络参数theta为什么要采用随机初始化？而不能初始化为0？</p>
<p>​        因为，对于一个神经网络，同一层的神经元都是同构的，他们拥有相同的输入输出，如果再将参数全部初始化为同样的值，那么无论前向传播还是反向传播的取值都是完全相同的。学习过程就无法打破这种对称性，最终同一网络层中各个参数仍然相同。可以考虑参数初始化取值范围$(-\frac{1}{\sqrt{d}}, \frac{1}{\sqrt{d}})$</p>
<p>•了解训练一个网络的过程步骤</p>
<h4 id="机器学习建议"><a href="#机器学习建议" class="headerlink" title="机器学习建议"></a>机器学习建议</h4><p><img src="C:%5CUsers%5Chwy%5CAppData%5CRoaming%5CTypora%5Ctypora-user-images%5Cimage-20191217191601019.png" alt="image-20191217191601019"></p>
<p><img src="C:%5CUsers%5Chwy%5CAppData%5CRoaming%5CTypora%5Ctypora-user-images%5Cimage-20191217191629587.png" alt="image-20191217191629587"></p>
<h4 id="聚类"><a href="#聚类" class="headerlink" title="聚类"></a>聚类</h4><p><img src="C:%5CUsers%5Chwy%5CAppData%5CRoaming%5CTypora%5Ctypora-user-images%5Cimage-20191217205900773.png" alt="image-20191217205900773"></p>
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                            <h6>NAVIGATION</h6>
                            <ol class="toc"><li class="toc-item toc-level-4"><a class="toc-link" href="#线性回归"><span class="toc-text">线性回归</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#逻辑回归"><span class="toc-text">逻辑回归</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#正则化"><span class="toc-text">正则化</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#神经网络"><span class="toc-text">神经网络</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#机器学习建议"><span class="toc-text">机器学习建议</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#聚类"><span class="toc-text">聚类</span></a></li></ol>
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